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There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

The promulgation of group purchasing organizations (GPOs) into the healthcare (HC) sector is an invaluable procurement strategy to manage the suppliers effectively. This study aims to identify and prioritize the factors of integrating GPOs into the HC sector on the perspectives of the developing countries such as India.

The factors are identified from current literature exploration, experts’ support and experience surveys. The factors are scrutinized and shortlisted using the Delphi technique and analysed further using the best-worst model method.

The findings of the study highlight the cost reduction, fair distribution of savings and healthcare supply chain (HCSC) data standardization among others to be the most prioritized drivers. The consulting services provided by GPOs including training and development as a result of high competitiveness in the HC market has been prioritized the least.

The study bears some important implications for decision and policymakers. The managers should consider factors, namely, cost reduction, fair distribution of savings and HCSC data standardization on a priority basis that acts as motivation for the HC providers to join the GPOs.

The study provides valuable insights for HC providers to participate in the GPOs for cost savings and enhance the performances.

The study aims to envisage upon conceptualizing and developing the scales of smart health-care supply chain (HCSC) performance in the era of the fourth industrial revolution.

This study has implemented structural equation modelling to analyse the survey data. To analyse the collected data from the field investigation involving a sample size of 323, the IBM SPSS AMOS 26 software package is considered to implement exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in this study.

The measurement model of the study developed using EFA and CFA has resulted in validating 32 items out of the 42 items. Resultantly, the analysis using the above-mentioned tools and the parsimony of items to scale development makes it more susceptible to contributing significantly to the current HCSC literature.

The HC providers need to consider a holistic and systematic approach while taking into account the constructs of smart HCSC performance, specifically, the effect of HCSC responsiveness and industry 4.0 between the independent and dependent variables. The scales are validated from the perspectives of developing countries such as India, and hence, their generalizability with respect to first-world countries is practically limited.

The scales validated in this study would facilitate managers and key decision-makers to apply the various elements of HCSC practices, gauge the application of these scales and monitor the performance of health-care facilities.

The study aims to identify and analyse the drivers of resilient healthcare supply chain (HCSC) preparedness in emergency health outbreaks to prevent disruption in healthcare services delivery in the context of India.

The present study has opted for the grey clustering method to identify and analyse the drivers of resilient HCSC preparedness during health outbreaks into high, moderate and low important grey classes based on Grey-Delphi, analytic hierarchy process (AHP) and Shannon's information entropy (IE) theory.

The drivers of the resilient HCSC are scrutinised using the Grey-Delphi technique. By implementing AHP and Shannon's IE theory and depending upon structure, process and outcome measures of HCSC, eleven drivers of a resilient HCSC preparedness are clustered as highly important, three drivers into moderately important, and two drivers into a low important group.

The analysis and insights developed in the present study would help to plan and execute a viable, resilient emergency HCSC preparedness during the emergence of any health outbreak along with the stakeholders' coordination. The results of the study offer information, rationality, constructiveness, and universality that enable the wider application of AHP-IE/Grey clustering analysis to HCSC resilience in the wake of pandemics.

The aim of this study is to examine the effects of innovativeness (INN), proactiveness, (PRC) and risk-taking (RIT) on the export performance of apparel small and medium-sized enterprises (SMEs) and the role of differentiation and low-cost leadership (LCL) strategies as mediating effects between entrepreneurial orientation (EO) dimensions and the performance of exporting firms. INN, RIT and PRC are considered EO dimensions.

A cross-sectional survey was carried out by providing a questionnaire to the owners, directors and senior managers of the apparel SMEs – the primary data of 550 treated by structural equation modeling (SEM) technique for final data analysis.

The study has revealed the positive dimensional effect of EO on export performance. For the mediation effects of differentiation and LCL, differentiation strategy (DS) positively mediates between INN, PRC and export performance. However, no mediation has been found between RIT and export performance. On the other hand, LCL has found positive effects between INN, RIT and export performance. However, the mediation effect was absent between PRC and export performance.

Limitations/implications- This study has been conducted on only Muslim owners, senior export managers and directors of apparel SMEs in Bangladesh. It has examined the two main competitive strategies as a mediator between EO dimensions and export performance. The findings of this study are based on one country data analysis.

EO, differentiation and low-cost leadership (LCL) strategy are resources and capabilities of an organization to create a competitive advantage to enhance performance. The factors of this research are helpful for SME practitioners.

The direct and indirect effects (differentiation and LCL strategy) of EO dimensions on export performance in an emerging country, i.e. the South-Asia region, is a pioneer study. Therefore, current research has theoretical and managerial implications for the international business and strategic management literature.

Although wearable health-monitoring technology (WHMT) has become a stimulus for public health, women’s acceptance rate of this technology appears to be low. Thus, this study intends to investigate the factors affecting women’s adoption of WHMT.

The unified theory of acceptance and use of technology–2 model has been used in this study as a research framework that has been extended to include lifestyle and attitude. The proposed extended framework is validated using primary data (n = 314) collected from female respondents using a structured questionnaire; the partial least square-based structural equation modeling technique is subsequently used to test the proposed hypothesis.

The results show that effort expectancy, social influence, price value, habit, attitude and lifestyle have significant positive effects on women’s behavioral intention to use WHMT and accelerate actual usage behavior. Notably, effort expectancy and habit exhibit the largest impact on behavioral intention. However, performance expectancy, facilitating conditions and hedonic motivation are not significantly associated with behavioral intentions.

The findings of this study are important for healthcare practitioners and service providers to comprehensively understand the factors that affect women’s behavioral intentions in line with their actual usage behavior. This insight will help policymakers design viable strategies regarding WHMT to promote its sustainable usage in least developed countries.

This study contributes novelty by using an extended model that links women’s attitudes and lifestyles to their adoption of WHMT. This study also fills the gaps in the existing literature on women’s behavioral intentions in the context of WHMT by showing novel associations in the domain of WHMT uptake.

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

The purpose of this paper is to focus on managing the risk of fraud in commercial letters of credit (LC) in Bangladesh involving three parties: the seller, the buyer and the bank. It addresses the severity of LC fraud, the banks’ actions when detected and the preventive measures the relevant parties can adopt.

This research uses doctrinal and qualitative methods to propose strategic actions that benefit buyers, sellers, banks, legal professionals and judges. The study aims to explore the modus operandi used by fraudsters through thematic analysis.

The study’s findings reveal that LC fraud has escalated to a concerning level, posing a significant threat to the economic stability of Bangladesh. Measures must be taken to mitigate this risk and safeguard the country’s financial integrity. To effectively combat the risk of LC fraud, the updated version of UCP must include specific and detailed guidelines on LC fraud. This study recommends preventative measures that all parties involved must take to reduce the likelihood of fraud significantly.

Due to a lack of LC experts, the participant sample for the study in Bangladesh was limited. Nevertheless, most banking participants were highly distinguished and held the Head of Trade Finance Department position in commercial banks. A few academics and legal practitioners with LC expertise also participated in the study.

It provides cutting-edge solutions to effectively handle LC fraud risk and provides proactive measures to prevent it.

Fake news on social media about COVID-19 pandemic and its associated issues (e.g. lockdown) caused public panic that lead to supply chain (SC) disruptions, which eventually affect firm performance. The purpose of this study is to understand how social media fake news effects firm performance, and how to mitigate such effects.

Grounded on dynamic capability view (DCV), this study suggests that social media fake news effects firm performance via SC disruption (SCD) and SC resilience (SCR). Moreover, the relation between SCD and SCR is contingent upon SC learning (SCL) – a moderated mediation effect. To validate this complex model, the authors suggest effectiveness of using partial least squares structural equation modeling (PLS-SEM). Using an online survey, the results support the authors’ hypotheses.

The results suggest that social media fake news does not affect firm performance directly. However, the authors’ serial mediation test confirms that SCD and SCR sequentially mediate the relationship between social media fake news and firm performance. In addition, a moderated serial mediation test confirms that a higher level of SCL strengthens the SCD–SCR relationship.

This work offers a new theoretical and managerial perspective to understand the effect of fake news on firm performance, in the context of crises, e.g. COVID-19. In addition, this study offers the advancement of PLS as more robust for real-world applications and more advantageous when models are complex.

Prior studies in the SC and marketing domain suggest different effects of social media fake news on consumer behavior (e.g. panic buying) and SCD, respectively. This current study is a unique effort that investigates the ultimate effect of fake news on firm performance with complex causal relationships via SCD, SCR and SCL.

This study intends to explore the connection between Facebook-based social media marketing (FSMM) and Facebook-based online purchase order (FOPO) for 20 popular online fashion retail brands across three South Asian countries: India, Pakistan and Bangladesh. FSMM was further divided into four components: Perceived trust (PT), Perceived informativeness (PInf), Perceived interactivity (PInt) and Perceived benefit (PB). 

The authors selected 20 popular Facebook-based online fashion brands involved in clothing and fashion accessories businesses in those three countries. Later, the authors purposively selected 114 region-based Facebook page administrators (admins) responsible for operating those brands' Facebook pages and taking Facebook-based online orders. The authors collected primary data from those admins as respondents through a structured survey instrument. The authors applied SPSS 25 for descriptive analysis and a covariance-based structural equation modeling (CB-SEM) (through AMOS 25) for testing the hypothesized relations.

Based on the valid responses and application of proper statistical measures, it was revealed that three FSMM components: PT, PInf and PB have significant positive relationships with FOPO, while PInt has an insignificant relationship with FOPO. 

South Asia is a growing business hub and the largest consumer market in terms of population. This study was conducted to identify the relationship between FSMM and FOPO in the three most prominent South Asian countries. As the first study was undertaken ever on customer perceptions of FSMM in a multi-country South Asian context, this paper is expected to be helpful for academics in conducting further empirical investigations on Facebook-based marketing as well as practitioners and policymakers in formulating and implementing Facebook-based marketing strategies.